





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase        =logging.get_logger(__name__)

__UpperCAmelCase        ={
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class       lowerCAmelCase__				(   UpperCAmelCase_  ):
 lowercase__     :  Optional[Any]				=   'canine'







 def __init__(   self		,	UpperCamelCase__=7_68		,	UpperCamelCase__=12		,	UpperCamelCase__=12		,	UpperCamelCase__=30_72		,	UpperCamelCase__="gelu"		,	UpperCamelCase__=0.1		,	UpperCamelCase__=0.1		,	UpperCamelCase__=1_63_84		,	UpperCamelCase__=16		,	UpperCamelCase__=0.02		,	UpperCamelCase__=1e-12		,	UpperCamelCase__=0		,	UpperCamelCase__=0Xe000		,	UpperCamelCase__=0Xe001		,	UpperCamelCase__=4		,	UpperCamelCase__=4		,	UpperCamelCase__=8		,	UpperCamelCase__=1_63_84		,	UpperCamelCase__=1_28		,	**UpperCamelCase__		,	):

    '''simple docstring'''


    super().__init__(pad_token_id=UpperCamelCase__		,	bos_token_id=UpperCamelCase__		,	eos_token_id=UpperCamelCase__		,	**UpperCamelCase__					)

    A__											=					max_position_embeddings
    A__											=					hidden_size
    A__											=					num_hidden_layers
    A__											=					num_attention_heads
    A__											=					intermediate_size
    A__											=					hidden_act
    A__											=					hidden_dropout_prob
    A__											=					attention_probs_dropout_prob
    A__											=					initializer_range
    A__											=					type_vocab_size
    A__											=					layer_norm_eps

    # Character config:
    A__											=					downsampling_rate
    A__											=					upsampling_kernel_size
    A__											=					num_hash_functions
    A__											=					num_hash_buckets
    A__											=					local_transformer_stride



from __future__ import annotations

import numpy as np
def 							UpperCamelCase_(					_A :list[float]	)->		Optional[Any]:
			return np.maximum(0					,  _A	)


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


